Application of Taguchi Method for Optimization of Process Parameters in Drilling Operation

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume: 3 | Issue: 4 | May-Jun 2019 Available Online: www.ijtsrd.com e-ISSN: 2456 - 6470
@ IJTSRD | Unique Paper ID - IJTSRD24003 | Volume – 3 | Issue – 4 | May-Jun 2019 Page: 1052
Application of Taguchi Method for Optimization of
Process Parameters in Drilling Operation
R. Manohara1, Mr. A. Harinath2
1P.G Scholar, 2Assistant Professor
1,2Department of mechanical engineering,
1,2Sri Venkateswara institute of technology, Anantapur, Andhra Pradesh, India
How to cite this paper: R. Manohara |
Mr. A. Harinath "Application of Taguchi
Method for Optimization of Process
Parameters in Drilling Operation"
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ABSTRACT
Taguchi Method is a statistical approach to optimizetheprocessparametersand
improve the quality of components that are manufactured. The objective of this
work is to illustrate the procedure adopted in using Taguchi Method to adrilling
operation. The OA , S/N ratio, and the ANOVA are employed to study the
performance of drilling operation.
In this analysis, three factors namely speed; feed and depth of cut were
considered. Accordingly, a suitable orthogonal array was selected and
experiments were conducted. After conducting the experiments the MRR and
Surface roughness was measured and Signal to Noise ratio was calculated. With
the help of graphs, optimum valueswereobtained and confirmationexperiments
were carried out.
These results were compared with the results of previous work . It reports that
research relating to improving performance measures, optimizing the process
parameters. The published paper also discusses the future trend of research
work in the same area.
Keywords: ANNOVA, DOE, OA, S/N RATIO, TAGUCHI METHOD
INTRODUCTION
Taguchi method is a statisticalmethoddeveloped by Taguchiand Konishi.Initially
it was developed for improving the quality of goods manufactured
(manufacturing process development),
later its application was expanded to many other fields in
Engineering, such as Biotechnology etc. Professional
statisticians have acknowledged Taguchi’s efforts especially
in thedevelopment of designs for studying variation.Success
in achieving thedesired results involves a carefulselectionof
process parameters and bifurcating them into control and
noise factors.
Selection of controlfactors mustbemadesuchthatitnullifies
the effect of noise factors. Taguchi Method involves
identification of proper controlfactorstoobtaintheoptimum
results of the process. Orthogonal Arrays (OA) are used to
conduct a set of experiments. Results of these experiments
are used to analyze the data and predict the quality of
components produced.
Here, an attempt has been made to demonstrate the
application of Taguchi’s Method to improvethesurfacefinish
characteristics of faced componentsthatwereprocessedona
lathe machine. Surface roughness is a measure of the
smoothness of a products surface and it is a factor that has a
high influence on the manufacturing cost. Surface finish also
affects the life of any product and hence it is desirable to
obtain higher grades of surface finish at minimum cost.
APPROACH TO PRODUCT/PROCESS DEVELOPMENT
Many methods have been developed and implemented over
the years to optimize the manufacturing processes. Some of
the widely used approaches are as given below:
Build-Test-Fix
The “Build-test-fix” is the most primitive approach which is
rather inaccurate as the process is carried out according to
the resources available, instead of tryingtooptimizeit.Inthis
method the process/product is tested and reworked each
time till the results are acceptable.
One Factor at a Time
The “one-factor-at-a-time” approach is aimed at optimizing
the process by running an experiment at one particular
condition and repeating the experiment by changing any
other one factor till the effect of all factors are recorded and
analyzed. Evidently, it is a very time consuming and
expensive approach. In this process, interactions between
factors are not taken in to account.
Design of Experiments Application of Taguchi
Method for Optimization of Process Parameters in
Improving the Surface
Conventional machining in which a sharpcutting tool is used
to mechanical cut the material to achieve the desired shape,
IJTSRD24003

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
size and geometry. The predominant cutting action in
machining involves shear deformation of the work material
to form a various kinds of chips; as the chips removed, a new
surface is exposed, that is called as machined surface.
Machining is a most frequently applied to shape metals.
DRILLING MACHINE
Drilling is a most common and complex used industrial
machining processes of creating and originating a hole in
mechanical components and workpiece.Thetoolused,called
a drill and the machine tool used is called a drill machine.
Drilling can also be define as a rotary end-cutting tool having
oneor more cuttingedges called lips, and havingoneormore
helical or straight flutes for the passage of chips and passing
the cutting fluid to the machining zone.
Rotating drill fed into the stationary work piece to form a
hole whose diameter is determined by the drill diameter.
Drilling makes up about 25% of all the machining processes
performed. Drilling is really a Complex Process, because
Only exit for the chips is the hole that filled by the drill.
Friction results in heat in addition to that due to chip.
Counterflowof chips makes lubrication and cooling difficult.
KINEMATIC SYSTEM OF GENERAL PURPOSE DRILLING
MACHINE AND THEIR PRINCIPLE OF WORKING
Kinematic system in any machine tool is comprised of chain
of several mechanisms to enable transform and transmit
motion from the power source to the cutting tool and the
work piece for the desired machining action.
The kinematicstructure varies frommachinetooltomachine
tool requiring different type and number of tool-work
motions. Even for the same type of machine tool, say column
drilling machine, the designer may take different kinematic
structure depending upon productivity, process capability,
durability, compactness, overall cost etc targeted. Typical
kinematicsystem of avery general-purposedrillingmachine
are, a column-drilling machine having 12 spindle speeds and
6 feeds. The kinematic system enables the drilling machine
the following essential works as:
Cutting Motion
Feed Motion
Tool Work Mounting
TWIST DRILLS
Drill bits are cutting tools usedtocreatecylindricalholes.Bits
held in a tool called a drill, which rotates them and provides
torque and axial force to create thehole.Differentpointangle
drills and different diameter drills and of different length of
drills can be used according to the application of work.
LITERATURE REVIEW
Gaitonde, Karnik and Davim carried out study on drilling of
LAMIPAN PB (wood coating layer) Medium Density Fibre
board panel using cuttingconditions i.e. spindlespeed&feed
rate to minimize the delamination tendency Using Response
Surface Methodology and Taguchi Design & by forming L9
Orthogonal Array on 16 mm thickness panel and 5 mm
diameter panel. Cemented Carbide drills (K20 grade, 20º
helix and 60º point angle) were, employed for the
experimentation. Result of Response surface analysis clearly
indicates it is necessary to employ low values of feed rate
along with the higher values of cutting speed.
METHODOLOGY
OBJECTIVE OF THE PRESENT WORK
The objective of the present work is to find outmain effectof
cutting speed, feed rate, drill diameters, work piecematerial,
drill material and interaction effect between drill material
and cutting speed on MRR, Surfaceroughness, Holediameter
error, and burr height. Microstructureanalysisof workpiece
material also did. The formula used for measuring the MRR
are given below
MRR is given by:
MRR = (Wi-Wf)/tgm3/min
Wi =Initial weight of work piece material in gram
Wf =Final weight of work piece material in gram
t=Time period of machining in minutes
DEGREES OF FREEDOM (DOF)
Total degree of freedom required for the entire
experimentation determined by the number of factors, their
interactions effects and level for factors. The degree of
freedom for each factor is given by the number of levels
minus one.
DOF for each factor = k-1
Where k is the number of level for each factor
DOF for interactions between factors: (kA-1) × (kB-1) Where
kA and kB are number of level for factor A and B
ORTHOGONAL ARRAY
OA derived from factorial design of experimentby aseries of
very sophisticated mathematical algorithms including
combinatory, finite fields, geometry and error correcting
codes. OA plays a critical part in achieving thehighefficiency
of the Taguchi method. The OA is constructed in a
statistically independent manner Within each column,
number of occurrences of each level is equal and for each
level within one column, each level within any other column
will occur an equal number of times as well.
Then, the columns are called orthogonal to each other. OA is
available with a variety of factors and levels in the Taguchi
method. Since each column is orthogonal to the others, if the
results associated with one level of aspecificfactor aremuch
different at another level, it is because changing that factor
from one level to the next has strong impact on the quality
characteristic being measured. Since the levels of the other
factors are occurring an equal number of times for eachlevel
of the strong factor, any effect by these other factors will be
ruled out. The selection of orthogonal array will depend on:
The number of factors and interactions of interest
The number of levels for the factors of interest
Taguchi orthogonal arrays are experimental designs that
usually require only a fraction of the full factorial
combinations. The columns of arrays are balanced and
orthogonal. This means that in each pair of columns, all
factor combinations occur same number of times.
Orthogonal designs allow estimating the effectof each factor
on the response independently of all other factors. Once the
degrees of freedom are known, the next step is to select the
orthogonal array (OA). The number of treatment conditions
is equal to the number of rows in the orthogonal array and it
must be equal to or greater than the total degrees of
freedom. Once the appropriate orthogonal array has been
selected, the factor can be assigned to the various columns .
L18 Linear graph is shown in figure that’s used in
experiment and experimental design of L18 is shown in
Table.

Table 1: L18 Experimental design
Trial no Tool material Cutting speed (RPM) Feed (mm/rev) Drill diameter (mm) Work-piece material
1 M2 HSS 80 0.10 4 EN 31
2 M2 HSS 80 0.125 8 H 11
3 M2 HSS 80 0.150 12 HCHCr
4 M2 HSS 160 0.10 4 H 11
5 M2 HSS 160 0.125 8 HCHCr
6 M2 HSS 160 0.150 12 EN 31
7 M2 HSS 244 0.10 8 EN 31
8 M2 HSS 244 0.125 12 H 11
9 M2 HSS 244 0.150 4 HCHCr
10 M35 HSS 80 0.10 12 HCHCr
11 M35 HSS 80 0.125 4 EN 31
12 M35 HSS 80 0.150 8 H 11
13 M35 HSS 160 0.10 8 HCHCr
14 M35 HSS 160 0.125 12 EN 31
15 M35 HSS 160 0.150 4 H 11
16 M35 HSS 244 0.10 12 H 11
17 M35 HSS 244 0.125 4 HCHCr
18 M35 HSS 244 0.150 8 EN 31
EXPERIMENTAL SET UP
The experiments have been conducted on Radial drilling machine shown in (Figure 3.2) available in the Machine Tool lab.
Many input parameters like work piece material, cutting speed, feed, drill diameter and drill material has been varied in this
experiment. Each factors has its own effect on the output parameters such as Material removal rate (MRR), Surfaceroughness
(SR), hole diameter error and burr height. The input parameters, which kept constant duringtheexperimentation, aregivenin
the Table 3.4 Before start of experiment, the work piece material ground to remove any dust, durt particles or removing any
surface defect and tapering effect of work piece.
Table 2: Constant input parameters for a drilling machine
Sr. No. Parameters Value
1 Machining time 60 sec
2 Point angle 118°
3 Helix angle 32°
4 Shank type Cylindrical
5 No of flutes 2
MEASURING AND TESTING EQUIPMENT USED
Surface roughness tests conducted on all the 18 samples produced by the radial drilling machine.MRRwasmeasured usingan
electric balance weighing machine which has a resolution of 0.01 mg, whereas Burr height was measured using a digital
Calliper which has a resolution of 0.01 mm. The details of important equipment used for the test in the experimentalstudy are
given below.
Surface Roughness Tester
Surface roughness test of all the samples measured by contact type stylus Model. The accuracy of this device is 0.01 µm. To
measure the surface roughness, the blocks were sliced to the holes axes. The surface roughness was measuredparallelto each
hole axis and the average values of the surface roughness is taken.
The tools measuring surface roughness with probes, measure, and control in appropriate length and circumference theprobe
comes in and out holes while traveling on the surface. This movement is turned into electrical current by means of a coil or
crystal. After increasing the current by using suitable units, its value is shown with a pointer or digitally.
METHODOLOGY
The Taguchi methodology is one of the optimizing techniques that based on the design of experiments (DOE) approach. The
experiments analysis will propose to conduct using the design of experiments technique. Althoughfullfactorialdesignscanbe
use where in all the possible combinations can be test, we would use fractional factorial analysis methods for the experiment.
The Taguchi Design is a design of experiment (DOE) approach developed byDr. GenichiTaguchiinorder toimprovethequality
of manufactured goods in Japan. Although similar to factorial design of experiment, the Taguchi design only conducts
balanced(orthogonal) experimental combinations, which makes the Taguchi design even more efficient than a fractional
factorial design. The Taguchi methodology has been proposed to overcome the limitations of full factorial analysis by
simplifying and standardizing the fractional factorial design .

Figure 1 the system (a) ideal function (b) the real system
Table: 3 Result for Surface roughness
Trial
No
Tool
Material
Speed
(RPM)
Feed
(mm/rev)
Drill
Diameter
(mm)
Work-
Piece
Surface
Roughness (µm) Mean Surface
roughness(µm)
S/N Ratio
I II
1 M2 80 0.10 4 EN 31 0.65 0.73 0.69 3.223018
2 M2 80 0.125 8 H 11 0.77 0.65 0.71 2.974833
3 M2 80 0.150 12 HCHCr 0.99 1.13 1.06 -0.50612
4 M2 160 0.10 4 H 11 0.98 0.60 0.79 2.047458
5 M2 160 0.125 8 HCHCr 0.89 0.99 0.94 0.537443
6 M2 160 0.150 12 EN 31 1.10 1.06 1.08 0.66848
7 M2 244 0.10 8 EN 31 0.78 0.70 0.74 2.615366
8 M2 244 0.125 12 H 11 0.88 1.08 0.98 0.175478
9 M2 244 0.150 4 HCHCr 1.15 0.81 0.98 0.175478
10 M35 80 0.10 12 HCHCr 1.02 1.02 1.02 -0.172
11 M35 80 0.125 4 EN 31 0.56 0.82 0.69 3.223018
12 M35 80 0.150 8 H 11 0.79 0.61 0.70 3.098039
13 M35 160 0.10 8 HCHCr 0.89 0.83 0.86 1.310031
14 M35 160 0.125 12 EN 31 0.93 0.75 0.84 1.514414
15 M35 160 0.150 4 H 11 0.75 0.77 0.76 2.383728
16 M35 244 0.10 12 H 11 0.84 0.96 0.90 0.91515
17 M35 244 0.125 4 HCHCr 0.99 1.05 1.02 -0.172
18 M35 244 0.150 8 EN 31 0.86 0.88 0.87 1.209615
PROCEDURES OF TAGUCHI METHOD
Figure 2- Taguchi design procedure
RESULTS FOR SURFACE ROUGHNESS (RA)
In this study surface roughness of 18 experimental trials with repetition has measured for each sample. For measuring surface
roughness, the samplinglength is taken as 3 mm and cutoff length is taken as 0.8 mm.Theresultsforsurfaceroughnessforeach
of the 18 experimental trials with repetition are given in Table .
Table4.Analysis Of Variance - Surface Roughness:
Sources SS v V F P %contribution Status
Work piece (A) 11.5405 2 5.7703 30.68 0.001 33.76 Significant
Feed (B) 1.5242 2 0.7621 4.05 0.077 Insignificant
Dill diameter (C) 11.2950 2 5.6475 30.03 0.001 32.99 Significant
Drill material (D) 0.4157 1 0.4157 2.21 0.188 Insignificant
Cutting speed (E) 4.1676 2 2.0838 11.08 0.010 10.48 Significant
Drill material×Cuttingspeed(F) 1.5963 2 0.7981 4.24 0.071 10.02 Insignificant
Residual Error 1.1285 6 0.1881 3.22 0.014 9.64 Insignificant
Total 31.6678 1 100

The results were analysed using ANOVA foridentifyingthesignificantfactorsaffectingtheperformancemeasures. TheAnalysis
of Variance (ANOVA) for the mean surface roughness at 95% confidence interval is given in Table. The variation data for each
factor were F-tested to find significance of each.
The principle of the F-test is that the larger the F value for a particular parameter, the greater the effect on the performance
characteristic due to the change in that process parameter. ANOVA table shows that work piece material with F value of 26.47,
drill diameter with F value of 26.61 and cutting speed with F value of 7.74 are the factors that significantly affect the surface
roughness.
Figure 3: Main effect plot for Mean Surface roughness
Figure4: Interaction plot for Mean Surface Roughness
RESULTS FOR S/N RATIO – SURFACE ROUGHNESS
The S/N ratiois an indication of the amount of variation present in the process. The S/N ratios havebeen calculatedtoidentify
themajor contributing factors thatcause variation in surface roughness. Surfaceroughnessisa“lowerthebetter”typeresponse
and it is given by a logarithmic function based on the mean square deviation:
Table: 5 ANOVA for Surface Roughness
Sources SS V V F P %contriButio Status
Feed (B) 0.017100 2 0.008550 4.02 0.078 31.02 Insignificant
Drill material (D) 0.005339 1 0.005339 2.51 0.164 32.20 Insignificant
Cutting speed(E) 0.032933 2 0.016467 7.74 0.022 7.55 Significant
Drillmaterial ×Cutting 0.016844 2 0.008422 3.96 0.080 6.23 Insignificant
Total 0.310850 17 0.00103 3.12 0.002 100 Insignificant
E pooled 0.05205 11 0.00473 25.88 Insignificant
Table:6 Response table for Signal to Noise Ratios of Surface roughness
Sources SS V V F P %contriButio Status
Feed (B) 0.017100 2 0.008550 4.02 0.078 31.02 Insignificant
Drill material (D) 0.005339 1 0.005339 2.51 0.164 32.20 Insignificant
Cutting speed(E) 0.032933 2 0.016467 7.74 0.022 7.55 Significant
Drillmaterial ×Cutting 0.016844 2 0.008422 3.96 0.080 6.23 Insignificant
Total 0.310850 17 0.00103 3.12 0.002 100 Insignificant
E pooled 0.05205 11 0.00473 25.88 Insignificant

M35M2
2.0
1.5
1.0
0.5
0.0
24416080 0.1500.1250.100
1284
2.0
1.5
1.0
0.5
0.0
HCHCrH11EN31
MATERIAL
MeanofSNratios
SPEED FEED
DRILL DIAMETER WORKPIECE MATERIAL
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Smaller is better
Figure 5: Main effect plot for S/N ratio of Surface Roughness
24416080
2.0
1.5
1.0
0.5
M35M2
2.0
1.5
1.0
0.5
MATERIAL
SPEED
M2
M35
MATERIAL
80
160
244
SPEED
Interaction Plot for SNRA3
Data Means
Figure 6 Interaction plot for S/N ratio of Surface roughness
CONCLUSION
The effect of parameters i.e. cutting speed, feed rate, drill
diameters, work piece material, drillmaterialandinteraction
effect between drill material and cutting speed evaluated
using ANOVA design analysis and Regression analysis. The
purpose of the ANOVA was to identify the important
parameters in prediction of MRR, Surface Roughness . Some
results concluded from ANOVA and plots are given below:
Surface Roughness
The effects of parameters i.e. cutting speed, feed rate, drill
diameters, work piece material, drillmaterialandinteraction
effect between drill material and cutting speed were
evaluated using ANOVA analysis. A confidence interval of
95%has been usedfor the analysis.Onerepetitionforeachof
18 trails was completed to measure the Signal to Noise
ratio(S/N Ratio).
In this experiment work surface roughness (Ra) has
measured at position center. ANOVA table shows that work
piece material with F value 26.47, drill diameter with F value
26.61 and cutting speeds with F value 7.74 are the factors
that significantly affect the surface roughness.
The percentage contribution of these factors is 33.19%,
33.38%, 7.55%. All others factors,namely,feed,drillmaterial
×cutting speed and drill material were found to be
insignificant. The estimated mean values of roughness
considered with 95% confidence interval found tobe 0.8017
± 0.0667 µm.
REFERENCES
[1] Palanikumar K., Parkash S. and Shanmugan K., (2008),
“Evaluation of delamination in drilling GFRP
composites”, Materials and Manufacturingprocess,Vol.
23(8), pp. 858-864.
[2] TasoC. C., (2008 , “Prediction of thrustforceofstepdrill
in drilling composite material by Taguchi method and
radial basis function network”, International Journal of
Advanced Manufacturing Technology, Vol. 36, pp. 11–
18.
[3] Palanikumar K., (2010), “Modeling and Analysis of
Delamination factor & surface roughness in drilling
GFRP Composites”, Materials and Manufacturing
Processes, Vol. 25, pp. 1059-1067.
[4] Gaitonde V.N., Karnik S.R., Rubio J., A. Correia E., Abra
A.M. and Davim J., (2008),“Analysis of parametric
influence on delamination in high-speed drilling of
carbon fibre reinforced plastic composites”, Journal of
Materials Processing Technology, Vol. 203, pp. 31–438.
[5] Tsao C.C. and ocheng ., (2007 , “Effect of tool wear on
delamination in drilling composite materials”,
International Journalof Mechanical Sciences, Vol. 9, pp.
983–988.
[6] Hochenga H. and Tsao C.C., (2006 , “Effects of special
drill bits on drilling-induced delaminationofcomposite
materials”, International Journal of Machine Tools &
Manufacture, Vol. 46, pp. 1403–1416.

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